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Translational Shifts in Preclinical Models of Depression: Implications for Biomarkers for Improved Treatments

  • Chloe Slaney
  • Justyna K. Hinchcliffe
  • Emma S. J. RobinsonEmail author
Chapter
Part of the Current Topics in Behavioral Neurosciences book series (CTBN, volume 40)

Abstract

Understanding the neurobiology of major depressive disorder (MDD) remains one of the major challenges in neuroscience. The disease is heterogeneous in nature, and patients present with a varied symptom profile. Studies seeking to identify biomarkers for MDD diagnosis and treatment have not yet found any one candidate which achieves sufficient sensitivity and specificity. In this article, we consider whether neuropsychological impairments, specifically affective biases, could provide a behavioural biomarker. Affective biases are observed when emotional states influence cognitive function. These biases have been shown to influence a number of different cognitive domains with some specific deficits observed in MDD. It has also been possible to use these neuropsychological tests to inform the development of translational tasks for non-human species. The results from studies in rodents suggest that quantification of affective biases is feasible and may provide a reliable method to predict antidepressant efficacy as well as pro-depressant risk. Animal studies suggest that affective state-induced biases in learning and memory operate over a different time course to biases influencing decision-making. The implications for these differences in terms of task validity and future ideas relating to affective biases and MDD are discussed. We also describe our most recent studies which have shown that depression-like phenotypes share a common deficit in reward-related learning and memory which we refer to as a reward-induced positive bias. This deficit is dissociable from more typical measures of hedonic behaviour and motivation for reward and may represent an important and distinct form of reward deficit linked to MDD.

Keywords

Affective bias Animal model Antidepressant Predictive validity Pro-depressant Reward 

Notes

Acknowledgments

ESJR currently has research funding from the MRC, BBSRC, Wellcome Trust and academic research grants from Boehringer Ingelheim and Eli Lilly. She has also previously received research funding from MSD, Pfizer and GSK. Previous support which has contributed to the development of this work includes funding from RCUK and the British Pharmacological Society Integrative Pharmacology Fund. CS is funded by a Wellcome Trust doctoral training studentship. JKH is funded by a University of Bristol PhD Studentship.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chloe Slaney
    • 1
  • Justyna K. Hinchcliffe
    • 1
  • Emma S. J. Robinson
    • 1
    Email author
  1. 1.School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences BuildingUniversity WalkBristolUK

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